{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyroc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The below loads in a dataset comparing the performance of a risk score (Krebs-Goplerud) against common biomarkers for Ovarian cancer. This is the dataset originally used in the article by Delong, Delong, and Clarke-Pearson which described the statistical test implemented in pytest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: (49, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Total Protein</th>\n",
       "      <th>Krebs-Goplerud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Albumin  Total Protein  Krebs-Goplerud\n",
       "idx                                        \n",
       "1        3.0            5.8               0\n",
       "2        3.2            6.3               5\n",
       "3        3.9            6.8               7\n",
       "4        2.8            4.8               4\n",
       "5        3.2            5.8               7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load CSV\n",
    "data = pd.read_csv(\"tests/data/ovarian_cancer.csv\", index_col=\"idx\")\n",
    "\n",
    "# Imputation (Median)\n",
    "outcome = data.pop(\"outcome\")\n",
    "\n",
    "data.albumin = data.albumin.fillna(np.median(data.albumin.dropna()))\n",
    "data.total_protein = data.total_protein.fillna(np.median(data.total_protein.dropna()))\n",
    "\n",
    "data.rename(columns={\n",
    "    'albumin': 'Albumin',\n",
    "    'total_protein': 'Total Protein',\n",
    "    'total_score': 'Krebs-Goplerud'\n",
    "}, inplace=True)\n",
    "\n",
    "print(f'Dataset size: {data.shape}')\n",
    "# Show examples\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate ROC object\n",
    "\n",
    "The ROC object is used to calculate the area under the operator characteristic curve (AUROC).\n",
    "The first argument should be the binary target (0s and 1s). The second argument should be a dataframe with the predictions to compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc = pyroc.ROC(outcome, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions are stored in a dictionary attribute named `preds`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['Albumin', 'Total Protein', 'Krebs-Goplerud'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0., 5., 7., 4., 7., 5., 2., 5., 5., 4., 4., 6., 4., 5., 5., 6., 6.,\n",
       "       4., 6., 4., 4., 3., 2., 5., 5., 4., 4., 5., 6., 5., 4., 4., 3., 4.,\n",
       "       5., 3., 4., 5., 1., 3., 2., 2., 3., 3., 3., 4., 5., 3., 5.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(roc.preds.keys())\n",
    "roc.preds['Krebs-Goplerud']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value of the ROC\n",
    "\n",
    "Upon initialization, the ROC object has calculated the AUROC for each prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.72297297 0.6509009  0.68693694]]\n"
     ]
    }
   ],
   "source": [
    "print(roc.auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the predictions are stored in an OrderedDict, we can easily associate the AUC with each prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Albumin': 0.722972972972973, 'Total Protein': 0.6509009009009009, 'Krebs-Goplerud': 0.6869369369369369}\n"
     ]
    }
   ],
   "source": [
    "auc = {pred_name: roc.auc[0, i] for i, pred_name in enumerate(roc.preds.keys())}\n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also acquire confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['Albumin', 'Total Protein', 'Krebs-Goplerud'])\n",
      "[0.53737381 0.44915782 0.48093018] lower\n",
      "[0.90857213 0.85264398 0.8929437 ] upper\n"
     ]
    }
   ],
   "source": [
    "print(roc.preds.keys())\n",
    "print(roc.ci()[0], 'lower')\n",
    "print(roc.ci()[1], 'upper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot ROC curves\n",
    "\n",
    "The ROC object provides a convenient method for plotting ROC curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2592x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = roc.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistically comparing different predictions\n",
    "\n",
    "pyroc provides a method for comparing whether a given prediction has a statistically significantly different AUROC than another. The approach is described in an article by DeLong, DeLong, and Clarke-Pearson ([available here](https://doi.org/10.2307/2531595)). In the article, a general approach is described which allows comparing either two predictions directly, e.g. \"is prediction A better than prediction B?\", or comparing groups of predictions, e.g. \"is prediction A significantly different than predictions B or C?\". This is accomplished through a *contrast matrix*. We will demonstrate usage of the compare method through three common questions asked. As a reminder, we have three predictors in our example dataset: Albumin, Total Protein, and Krebs-Goplerud. We may thus ask:\n",
    "\n",
    "* Is Krebs-Goplerud better than Total Protein?\n",
    "* Is Krebs-Goplerud better than Albumin?\n",
    "* Is Krebs-Goplerud better than at least one of Albumin or Total Protein?\n",
    "\n",
    "Recall our predictions are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['Albumin', 'Total Protein', 'Krebs-Goplerud'])\n"
     ]
    }
   ],
   "source": [
    "print(roc.preds.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is Krebs-Goplerud different than Total Protein?\n",
    "\n",
    "The first question focuses on direct comparison of two predictions. We use a single-row contrast: [0, -1, 1].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7245508480195271 [[-0.16439724  0.23646931]]\n"
     ]
    }
   ],
   "source": [
    "p, ci = roc.compare(np.array([\n",
    "    [0, -1,  1],\n",
    "]))\n",
    "print(p, ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our p-value is 0.72, which does not reject the null hypothesis. The AUROC Krebs-Goplerud score is not significantly different from the AUROC of the Total Protein biomarker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is Krebs-Goplerud different than Albumin?\n",
    "\n",
    "Similar to above, we are only comparing two of the three predictors. However, we are changing which predictor we are comparing again.\n",
    "\n",
    "We use a single-row contrast: [-1, 0, 1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7009410788445647 [[-0.21994244  0.14787037]]\n"
     ]
    }
   ],
   "source": [
    "p, ci = roc.compare(np.array([\n",
    "    [-1, 0,  1],\n",
    "]))\n",
    "print(p, ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, no significant difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is Krebs-Goplerud better than at least one of Albumin and Total Protein?\n",
    "\n",
    "This comparison is identical to the comparison made in page 844 of the original article by DeLong, DeLong, and Clarke-Pearson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.422912563380643 [0.05063562 7.37775891]\n"
     ]
    }
   ],
   "source": [
    "p, ci = roc.compare(np.array([\n",
    "    [1, -1,  0],\n",
    "    [1,  0, -1]\n",
    "]))\n",
    "print(p, ci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If no contrast is provided and we have N classifiers C<sub>0,N-1</sub>, all classifiers in C<sub>1,N-1</sub> is compared to C<sub>0</sub> (same behaviour as example above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.422912563380643 [0.05063562 7.37775891]\n"
     ]
    }
   ],
   "source": [
    "p, ci = roc.compare()\n",
    "print(p, ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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